提出快速稳定共形预测方法，提升大规模预测效率与精度。
Leave-One-Out Stable Conformal Prediction
- 基于留一法稳定性，无需样本分割加速全共形预测
- 在合成与真实数据上表现优于现有方法，测试功效更优
- 适合需高效高可靠预测的场景，如医疗筛查
共形预测（CP）是分布无关的不确定性量化重要工具，但如何在计算效率与预测精度间平衡仍是挑战，尤其在多预测任务中。本文提出留一法稳定共形预测（LOO-StabCP），利用留一法稳定性，在不进行样本分割的情况下加速全共形预测。相比基于替换一法稳定性的现有方法RO-StabCP，本方法在处理大量预测请求时显著更快。我们为多种主流机器学习工具推导了稳定性边界：正则化损失最小化（RLM）、随机梯度下降（SGD）、核方法、神经网络及集成学习（bagging）。理论分析表明该方法成立，并在合成数据与真实数据上均表现出优越数值性能。应用于筛查问题时，其对训练数据的高效利用使测试功效优于基于分割共形的最先进方法。
原文摘要 · Abstract (English)
Conformal prediction (CP) is an important tool for distribution-free predictive uncertainty quantification. Yet, a major challenge is to balance computational efficiency and prediction accuracy, particularly for multiple predictions. We propose Leave-One-Out Stable Conformal Prediction (LOO-StabCP), a novel method to speed up full conformal using algorithmic stability without sample splitting. By leveraging leave-one-out stability, our method is much faster in handling a large number of prediction requests compared to existing method RO-StabCP based on replace-one stability. We derived stability bounds for several popular machine learning tools: regularized loss minimization (RLM) and stochastic gradient descent (SGD), as well as kernel method, neural networks and bagging. Our method is theoretically justified and demonstrates superior numerical performance on synthetic and real-world data. We applied our method to a screening problem, where its effective exploitation of training data led to improved test power compared to state-of-the-art method based on split conformal.
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